Predicting Session Length in Media Streaming

Session length is a very important aspect in determining a user's satisfaction with a media streaming service. Being able to predict how long a session will last can be of great use for various downstream tasks, such as recommendations and ad scheduling. Most of the related literature on user interaction duration has focused on dwell time for websites, usually in the context of approximating post-click satisfaction either in search results, or display ads. In this work we present the first analysis of session length in a mobile-focused online service, using a real world data-set from a major music streaming service. We use survival analysis techniques to show that the characteristics of the length distributions can differ significantly between users, and use gradient boosted trees with appropriate objectives to predict the length of a session using only information available at its beginning. Our evaluation on real world data illustrates that our proposed technique outperforms the considered baseline.Comment: 4 pages, 3 figure

Validating simulated interaction for retrieval evaluation

A searcher’s interaction with a retrieval system consists of actions such as query formulation, search result list interaction and document interaction. The simulation of searcher interaction has recently gained momentum in the analysis and evaluation of interactive information retrieval (IIR). However, a key issue that has not yet been adequately addressed is the validity of such IIR simulations and whether they reliably predict the performance obtained by a searcher across the session. The aim of this paper is to determine the validity of the common interaction model (CIM) typically used for simulating multi-query sessions. We focus on search result interactions, i.e., inspecting snippets, examining documents and deciding when to stop examining the results of a single query, or when to stop the whole session. To this end, we run a series of simulations grounded by real world behavioral data to show how accurate and responsive the model is to various experimental conditions under which the data were produced. We then validate on a second real world data set derived under similar experimental conditions. We seek to predict cumulated gain across the session. We find that the interaction model with a query-level stopping strategy based on consecutive non-relevant snippets leads to the highest prediction accuracy, and lowest deviation from ground truth, around 9 to 15% depending on the experimental conditions. To our knowledge, the present study is the first validation effort of the CIM that shows that the model’s acceptance and use is justified within IIR evaluations. We also identify and discuss ways to further improve the CIM and its behavioral parameters for more accurate simulations

Dynamic modeling of web purchase behavior and e-mailing impact by Petri net

In this article, the authors introduce Petri nets to model the dynamics of web site visits and purchase behaviors in the case of wish list systems. They describe web site activities and their transition with probability distributions and model the sequential impact of influential factors through links that better explain web purchase behavior dynamics. The basic model, which analyzes site connections and purchases to explain visit and purchase behavior, performs better than a classical negative binomial regression model. To demonstrate its flexibility, the authors extend the wish list Petri net model to measure the impact of e-mailing intervals on visit frequency and purchase.internet; wish list; e-mail; Petri net; dynamic model

An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy

Etsy is a global marketplace where people across the world connect to make, buy and sell unique goods. Sellers at Etsy can promote their product listings via advertising campaigns similar to traditional sponsored search ads. Click-Through Rate (CTR) prediction is an integral part of online search advertising systems where it is utilized as an input to auctions which determine the final ranking of promoted listings to a particular user for each query. In this paper, we provide a holistic view of Etsy's promoted listings' CTR prediction system and propose an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings. We obtain representations from texts and images by utilizing state-of-the-art deep learning techniques and employ multimodal learning to combine these different signals. We compare the system to non-trivial baselines on a large-scale real world dataset from Etsy, demonstrating the effectiveness of the model and strong correlations between offline experiments and online performance. The paper is also the first technical overview to this kind of product in e-commerce context

Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online